University College London , Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Bioengineering, Malet Place, London WC1E 6BT, United Kingdom ; University College London , Dementia Research Centre, Institute of Neurology, London, WC1N 3BG, United Kingdom.

Abstract

Most medical image registration algorithms suffer from a directionality bias that has been shown to largely impact subsequent analyses. Several approaches have been proposed in the literature to address this bias in the context of nonlinear registration, but little work has been done for global registration. We propose a symmetric approach based on a block-matching technique and least-trimmed square regression. The proposed method is suitable for multimodal registration and is robust to outliers in the input images. The symmetric framework is compared with the original asymmetric block-matching technique and is shown to outperform it in terms of accuracy and robustness. The methodology presented in this article has been made available to the community as part of the NiftyReg open-source package.